ClimateWins Optimization Grid/Random Search

01 Import datasets and libraries¶

In [105]:
#01.1 import libraries
import pandas as pd
import numpy as np
import seaborn as sns
import os
import operator
import time
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from numpy import argmax
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.tree import plot_tree
from sklearn import tree
from sklearn.model_selection import GridSearchCV
from scipy.stats import randint
from sklearn.model_selection import RandomizedSearchCV
In [58]:
#01.2 create path to ClimateWins Data
path=r'C:\Users\jonba\OneDrive\Desktop\Machine Learning\Section 2\02 Datasets\02 Manipulated Datasets'
In [60]:
#01.3 import observations
climate = pd.read_csv(os.path.join(path, 'observations_date_cleaned.csv'))
climate.shape
Out[60]:
(22950, 137)
In [81]:
#01.4 import observations
weather = pd.read_csv(os.path.join(path, 'predictions.csv'))
weather.shape
Out[81]:
(22950, 16)

02 Data Wrangling

In [65]:
#02.1 Reduce climate to single decade
climate_decade = climate[(climate['DATE'].astype(str).str[:4] >= '2010') & (climate['DATE'].astype(str).str[:4] <= '2019')]
climate_decade
Out[65]:
DATE MONTH BASEL_cloud_cover BASEL_humidity BASEL_pressure BASEL_global_radiation BASEL_precipitation BASEL_sunshine BASEL_temp_mean BASEL_temp_min ... STOCKHOLM_temp_max VALENTIA_cloud_cover VALENTIA_humidity VALENTIA_pressure VALENTIA_global_radiation VALENTIA_precipitation VALENTIA_sunshine VALENTIA_temp_mean VALENTIA_temp_min VALENTIA_temp_max
18263 20100101 1 8 0.93 0.9965 0.18 0.56 0.0 2.9 -0.2 ... -7.0 1 0.80 1.0121 0.43 0.00 6.0 1.7 -1.6 5.0
18264 20100102 1 3 0.74 1.0193 0.58 0.00 5.2 -1.5 -4.2 ... -7.1 1 0.82 1.0196 0.45 0.00 6.0 0.8 -4.5 6.2
18265 20100103 1 3 0.75 1.0226 0.61 0.00 6.3 -3.2 -6.0 ... -7.1 4 0.71 1.0196 0.36 0.41 1.6 0.5 -4.1 5.1
18266 20100104 1 5 0.83 1.0169 0.48 0.00 3.7 -4.9 -8.3 ... -3.6 2 0.67 1.0219 0.48 0.51 6.5 3.7 1.3 3.8
18267 20100105 1 7 0.78 1.0073 0.53 0.04 4.4 -4.0 -7.1 ... -5.5 4 0.78 1.0117 0.45 0.09 4.7 2.3 -0.7 5.4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
21910 20191227 12 8 0.92 1.0273 0.15 0.52 0.1 6.8 5.6 ... 0.2 5 0.82 1.0142 0.04 0.41 3.4 10.7 7.9 13.5
21911 20191228 12 7 0.86 1.0380 0.35 0.00 1.3 3.7 -0.6 ... -1.0 5 0.82 1.0142 0.20 0.41 3.4 10.7 7.9 13.5
21912 20191229 12 6 0.93 1.0387 0.61 0.00 4.9 -0.8 -2.7 ... 5.8 5 0.82 1.0142 0.24 0.41 3.4 10.7 7.9 13.5
21913 20191230 12 1 0.81 1.0354 0.64 0.00 6.5 0.5 -3.0 ... 9.6 5 0.82 1.0142 0.06 0.41 3.4 10.7 7.9 13.5
21914 20191231 12 5 0.87 1.0350 0.58 0.00 3.5 0.4 -1.3 ... 8.5 5 0.82 1.0142 0.10 0.41 3.4 10.7 7.9 13.5

3652 rows × 137 columns

In [83]:
#02.2 reduce weather to 2010s
weather_decade = weather[(weather['DATE'].astype(str).str[:4] >= '2010') & (weather['DATE'].astype(str).str[:4] <= '2019')]
weather_decade
Out[83]:
DATE BASEL_pleasant_weather BELGRADE_pleasant_weather BUDAPEST_pleasant_weather DEBILT_pleasant_weather DUSSELDORF_pleasant_weather HEATHROW_pleasant_weather KASSEL_pleasant_weather LJUBLJANA_pleasant_weather MAASTRICHT_pleasant_weather MADRID_pleasant_weather MUNCHENB_pleasant_weather OSLO_pleasant_weather SONNBLICK_pleasant_weather STOCKHOLM_pleasant_weather VALENTIA_pleasant_weather
18263 20100101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18264 20100102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18265 20100103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18266 20100104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18267 20100105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
21910 20191227 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21911 20191228 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21912 20191229 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21913 20191230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21914 20191231 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3652 rows × 16 columns

In [73]:
#02.3 show shape climate_decade
climate_decade.shape
Out[73]:
(3652, 135)
In [85]:
#02.4 drop DATE from pleasant_year
weather_decade.drop(columns = 'DATE', inplace = True)
weather_decade.shape

C:\Users\jonba\AppData\Local\Temp\ipykernel_8576\4052020079.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  weather_decade.drop(columns = 'DATE', inplace = True)
Out[85]:
(3652, 15)

03 Reshaping for Modeling

In [88]:
#03.1 define X
X = climate_decade
In [90]:
#03.2 define y
y = weather_decade
In [92]:
#03.3 Turn X and y from a df to arrays
X = np.array(X)
y = np.array(y)
In [94]:
#03.4 X shape
X.shape
Out[94]:
(3652, 135)
In [96]:
#03.5 y shape
y.shape
Out[96]:
(3652, 15)

04 Split Data

In [107]:
#04.1 split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state = 42)
In [109]:
#04.2 print shapes of train and test
print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)

(2739, 135) (2739, 15)
(913, 135) (913, 15)

05 Hyperparameter Optimization

05.1 Finding Optimized with Grid¶

In [123]:
#05.11 Create a RF classifier
model = RandomForestClassifier()
In [145]:
#05.12 define grid_space
grid_space = {
    'max_depth': [5, 10, 15],  # Focus on a smaller range
    'n_estimators': [100, 200, 300],  # Reduced number of estimators
    'max_features': [2, 4, 6, 8],  # Focus on fewer values
    'min_samples_leaf': [4, 5, 6],  # Smaller range
    'min_samples_split': [5, 10]  # Avoid using 1 (invalid in sklearn)
}
In [148]:
#05.12
start = time.time()
grid_search = GridSearchCV(estimator=model,param_grid=grid_space,cv=3,scoring='accuracy', verbose=3, n_jobs=-1)
#first term in () is the name of the model used two steps above
#second term in () param_grid passes the search space name grid_space
#cv is how many total rows you want in your data or folds 3 and 5 are common values
#scoring returns accuracy of cv fold
#verbose tells it how much to print out 0 no output, 1 brief output, 2 detailed, 3 all the detals printed out
#n_jobs how many processor cores model uses -1 if you don't know what your computer has, -2 is all but one cpu on your computer so won't freeze
grid_search = grid_search.fit(X_train, y_train)
print('Search took %s minutes' % ((time.time() - start)/60))

Fitting 3 folds for each of 216 candidates, totalling 648 fits
Search took 4.421167023976644 minutes
In [149]:
#05.13 Grid search part 3 results
print('Best GRID search hyperparameters are: '+str(grid_search.best_params_))
print('Best GRID search score is: '+str(grid_search.best_score_))

Best GRID search hyperparameters are: {'max_depth': 10, 'max_features': 8, 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 300}
Best GRID search score is: 0.5264695144213216

05.2 Random Search¶

In [158]:
# Random search cv part 1
rs_space={'max_depth':list(np.arange(10, 75, step=5)) + [None],
            'n_estimators':np.arange(100, 500, step=10),
            'max_features':randint(5,10),
            'criterion':['gini','entropy'],
            'min_samples_leaf':randint(1,4),
            'min_samples_split':np.arange(2, 10, step=1)
         }
In [160]:
# random search part 2 creating a RF classifier
clf2= RandomForestClassifier()
In [162]:
# random search part 3
start = time.time()
rf_random = RandomizedSearchCV(clf2, rs_space, n_iter=200, scoring='accuracy', verbose=3, n_jobs=-1, cv=3) #500
model_random = rf_random.fit(X_train, y_train)
print('Search took %s minutes' % ((time.time() - start)/60))

Fitting 3 folds for each of 200 candidates, totalling 600 fits
Search took 15.779540153344472 minutes
In [163]:
# random random part 4 search results
print('Best RANDOM search hyperparameters are: '+str(model_random.best_params_))
print('Best RANDOM search score is: '+str(model_random.best_score_))

Best RANDOM search hyperparameters are: {'criterion': 'entropy', 'max_depth': 45, 'max_features': 9, 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 470}
Best RANDOM search score is: 0.5560423512230741

05.3 best results both¶

In [141]:
#05.31 best grid search results
print('Best GRID search hyperparameters are: '+str(grid_search.best_params_))
print('Best GRID search score is: '+str(grid_search.best_score_))
# best random random search results
print('Best RANDOM search hyperparameters are: '+str(model_random.best_params_))
print('Best RANDOM search score is: '+str(model_random.best_score_))

Best GRID search hyperparameters are: {'max_depth': None, 'max_features': 7, 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}
Best GRID search score is: 0.5355969331872946
Best RANDOM search hyperparameters are: {'criterion': 'gini', 'max_depth': 60, 'max_features': 6, 'min_samples_leaf': 1, 'min_samples_split': 4, 'n_estimators': 360}
Best RANDOM search score is: 0.5286600949251552
In [150]:
#06 Random Forest Optimized Search Hyperparameters
In [166]:
#06.1 creating a RF classifier
clf = RandomForestClassifier(n_estimators = 470, max_depth=10, min_samples_split=3, min_samples_leaf=1, max_features=7) #can be written with or without max_depth

# Training the model on the training dataset
# fit function is used to train the model using the training sets as parameters
clf.fit(X_train, y_train)
Out[166]:
RandomForestClassifier(max_depth=10, max_features=7, min_samples_split=3,
                       n_estimators=470)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier(max_depth=10, max_features=7, min_samples_split=3,
                       n_estimators=470)
In [168]:
#06.2 performing predictions on the test dataset
y_pred = clf.predict(X_test)

# using metrics module for accuracy calculation
print('Model Accuracy: ', metrics.accuracy_score(y_test, y_pred))

Model Accuracy:  0.5345016429353778
In [170]:
#06.3 variables
pred = ['Bad weather','Good weather']
In [172]:
#06.4 show tree
fig = plt.figure(figsize=(80,40))
plot_tree(clf.estimators_[15], fontsize = 20, class_names=pred, filled=True)
Out[172]:
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In [173]:
#06.5 save decision tree image
fig.savefig(f'{path}/CW_RF_optimized_2.png')

07 Identify Important Features¶

In [175]:
#07.1
np.set_printoptions(threshold=np.inf)
newarray = clf.feature_importances_
#print(clf.feature_importances_.shape)
newarray
Out[175]:
array([3.78536169e-03, 3.56100987e-03, 2.99216124e-03, 3.03397719e-03,
       1.51648137e-02, 1.10684244e-02, 1.90414232e-02, 7.60895365e-03,
       3.54998811e-02, 3.62008882e-03, 3.15907451e-03, 3.55923004e-03,
       2.57583491e-03, 1.49836401e-02, 3.02155549e-03, 1.03097671e-02,
       3.83334665e-03, 1.02838794e-02, 3.64220249e-04, 0.00000000e+00,
       0.00000000e+00, 1.29176884e-03, 1.26908365e-02, 1.23787956e-03,
       1.71312044e-02, 5.83867427e-03, 2.52334035e-02, 2.47443728e-03,
       2.78833634e-03, 5.46256084e-03, 4.61873822e-03, 1.42167118e-02,
       5.26180429e-03, 1.20253552e-02, 3.23822293e-03, 3.47413629e-02,
       3.14577697e-03, 3.47516173e-03, 4.78409382e-03, 5.30288032e-03,
       1.72230434e-02, 7.23228832e-03, 1.73103547e-02, 5.79794201e-03,
       3.81268223e-02, 2.43219104e-03, 4.00511162e-03, 3.69794127e-03,
       4.29314336e-03, 8.77942043e-03, 3.58012489e-03, 9.26426924e-03,
       5.46779170e-03, 1.75925243e-02, 3.55971607e-03, 2.64134073e-03,
       2.54044840e-03, 2.82422274e-03, 5.65946572e-03, 3.00898137e-03,
       3.46164887e-03, 2.36016301e-03, 3.69152121e-03, 3.79779163e-03,
       4.35011974e-03, 3.49699515e-03, 5.02701818e-03, 1.50306385e-02,
       6.24086719e-03, 1.44447154e-02, 6.85965297e-03, 3.02121849e-02,
       3.86616968e-03, 5.39114510e-03, 4.23940644e-03, 9.12049811e-03,
       1.52024907e-02, 8.11597547e-03, 1.70013727e-02, 4.36338212e-03,
       3.16188354e-02, 2.34093353e-03, 4.27181484e-03, 2.26386592e-03,
       5.82546118e-03, 6.20436196e-03, 5.92338289e-03, 1.50428664e-02,
       7.00546848e-03, 1.92899223e-02, 3.51646262e-03, 3.23871753e-03,
       4.14773641e-03, 7.19800935e-03, 1.56422605e-02, 6.25081247e-03,
       2.17878993e-02, 8.74965199e-03, 2.38283257e-02, 2.22237857e-03,
       2.86421487e-03, 3.42316930e-03, 3.06397551e-03, 7.39050125e-03,
       3.35475195e-03, 1.12714995e-02, 6.03575697e-03, 1.70394777e-02,
       1.99556031e-03, 2.52375679e-03, 3.80590764e-03, 3.34477316e-03,
       3.68771291e-03, 3.88588654e-03, 5.40785190e-03, 3.99673233e-03,
       6.20982543e-03, 5.74892765e-04, 2.69147078e-03, 2.51432338e-03,
       5.86845954e-03, 5.83583318e-03, 3.61718731e-03, 9.61071556e-03,
       8.56804998e-03, 1.74117787e-02, 1.27516815e-04, 1.28647763e-04,
       8.70048466e-05, 2.76543092e-03, 2.36725587e-05, 1.42425347e-04,
       1.09014274e-04, 1.23254541e-04, 3.93046593e-04])
In [176]:
#07.2 
np.set_printoptions(threshold=1000)
In [177]:
#07.3
newarray = newarray.reshape(-1,15,9)
print(newarray.shape)
newarray

(1, 15, 9)
Out[177]:
array([[[3.78536169e-03, 3.56100987e-03, 2.99216124e-03, 3.03397719e-03,
         1.51648137e-02, 1.10684244e-02, 1.90414232e-02, 7.60895365e-03,
         3.54998811e-02],
        [3.62008882e-03, 3.15907451e-03, 3.55923004e-03, 2.57583491e-03,
         1.49836401e-02, 3.02155549e-03, 1.03097671e-02, 3.83334665e-03,
         1.02838794e-02],
        [3.64220249e-04, 0.00000000e+00, 0.00000000e+00, 1.29176884e-03,
         1.26908365e-02, 1.23787956e-03, 1.71312044e-02, 5.83867427e-03,
         2.52334035e-02],
        [2.47443728e-03, 2.78833634e-03, 5.46256084e-03, 4.61873822e-03,
         1.42167118e-02, 5.26180429e-03, 1.20253552e-02, 3.23822293e-03,
         3.47413629e-02],
        [3.14577697e-03, 3.47516173e-03, 4.78409382e-03, 5.30288032e-03,
         1.72230434e-02, 7.23228832e-03, 1.73103547e-02, 5.79794201e-03,
         3.81268223e-02],
        [2.43219104e-03, 4.00511162e-03, 3.69794127e-03, 4.29314336e-03,
         8.77942043e-03, 3.58012489e-03, 9.26426924e-03, 5.46779170e-03,
         1.75925243e-02],
        [3.55971607e-03, 2.64134073e-03, 2.54044840e-03, 2.82422274e-03,
         5.65946572e-03, 3.00898137e-03, 3.46164887e-03, 2.36016301e-03,
         3.69152121e-03],
        [3.79779163e-03, 4.35011974e-03, 3.49699515e-03, 5.02701818e-03,
         1.50306385e-02, 6.24086719e-03, 1.44447154e-02, 6.85965297e-03,
         3.02121849e-02],
        [3.86616968e-03, 5.39114510e-03, 4.23940644e-03, 9.12049811e-03,
         1.52024907e-02, 8.11597547e-03, 1.70013727e-02, 4.36338212e-03,
         3.16188354e-02],
        [2.34093353e-03, 4.27181484e-03, 2.26386592e-03, 5.82546118e-03,
         6.20436196e-03, 5.92338289e-03, 1.50428664e-02, 7.00546848e-03,
         1.92899223e-02],
        [3.51646262e-03, 3.23871753e-03, 4.14773641e-03, 7.19800935e-03,
         1.56422605e-02, 6.25081247e-03, 2.17878993e-02, 8.74965199e-03,
         2.38283257e-02],
        [2.22237857e-03, 2.86421487e-03, 3.42316930e-03, 3.06397551e-03,
         7.39050125e-03, 3.35475195e-03, 1.12714995e-02, 6.03575697e-03,
         1.70394777e-02],
        [1.99556031e-03, 2.52375679e-03, 3.80590764e-03, 3.34477316e-03,
         3.68771291e-03, 3.88588654e-03, 5.40785190e-03, 3.99673233e-03,
         6.20982543e-03],
        [5.74892765e-04, 2.69147078e-03, 2.51432338e-03, 5.86845954e-03,
         5.83583318e-03, 3.61718731e-03, 9.61071556e-03, 8.56804998e-03,
         1.74117787e-02],
        [1.27516815e-04, 1.28647763e-04, 8.70048466e-05, 2.76543092e-03,
         2.36725587e-05, 1.42425347e-04, 1.09014274e-04, 1.23254541e-04,
         3.93046593e-04]]])
In [182]:
#07.4
sumarray = np.sum(newarray[0], axis=1)
sumarray
Out[182]:
array([0.10175601, 0.05534642, 0.06378799, 0.08482753, 0.10239836,
       0.05911252, 0.02974751, 0.08945998, 0.09891928, 0.06816808,
       0.09435988, 0.05666573, 0.03485801, 0.05669271, 0.00390001])
In [184]:
#07.5 columns list
climate_nodate.columns.tolist()
Out[184]:
['BASEL_cloud_cover',
 'BASEL_humidity',
 'BASEL_pressure',
 'BASEL_global_radiation',
 'BASEL_precipitation',
 'BASEL_sunshine',
 'BASEL_temp_mean',
 'BASEL_temp_min',
 'BASEL_temp_max',
 'BELGRADE_cloud_cover',
 'BELGRADE_humidity',
 'BELGRADE_pressure',
 'BELGRADE_global_radiation',
 'BELGRADE_precipitation',
 'BELGRADE_sunshine',
 'BELGRADE_temp_mean',
 'BELGRADE_temp_min',
 'BELGRADE_temp_max',
 'BUDAPEST_cloud_cover',
 'BUDAPEST_humidity',
 'BUDAPEST_pressure',
 'BUDAPEST_global_radiation',
 'BUDAPEST_precipitation',
 'BUDAPEST_sunshine',
 'BUDAPEST_temp_mean',
 'BUDAPEST_temp_min',
 'BUDAPEST_temp_max',
 'DEBILT_cloud_cover',
 'DEBILT_humidity',
 'DEBILT_pressure',
 'DEBILT_global_radiation',
 'DEBILT_precipitation',
 'DEBILT_sunshine',
 'DEBILT_temp_mean',
 'DEBILT_temp_min',
 'DEBILT_temp_max',
 'DUSSELDORF_cloud_cover',
 'DUSSELDORF_humidity',
 'DUSSELDORF_pressure',
 'DUSSELDORF_global_radiation',
 'DUSSELDORF_precipitation',
 'DUSSELDORF_sunshine',
 'DUSSELDORF_temp_mean',
 'DUSSELDORF_temp_min',
 'DUSSELDORF_temp_max',
 'HEATHROW_cloud_cover',
 'HEATHROW_humidity',
 'HEATHROW_pressure',
 'HEATHROW_global_radiation',
 'HEATHROW_precipitation',
 'HEATHROW_sunshine',
 'HEATHROW_temp_mean',
 'HEATHROW_temp_min',
 'HEATHROW_temp_max',
 'KASSEL_cloud_cover',
 'KASSEL_humidity',
 'KASSEL_pressure',
 'KASSEL_global_radiation',
 'KASSEL_precipitation',
 'KASSEL_sunshine',
 'KASSEL_temp_mean',
 'KASSEL_temp_min',
 'KASSEL_temp_max',
 'LJUBLJANA_cloud_cover',
 'LJUBLJANA_humidity',
 'LJUBLJANA_pressure',
 'LJUBLJANA_global_radiation',
 'LJUBLJANA_precipitation',
 'LJUBLJANA_sunshine',
 'LJUBLJANA_temp_mean',
 'LJUBLJANA_temp_min',
 'LJUBLJANA_temp_max',
 'MAASTRICHT_cloud_cover',
 'MAASTRICHT_humidity',
 'MAASTRICHT_pressure',
 'MAASTRICHT_global_radiation',
 'MAASTRICHT_precipitation',
 'MAASTRICHT_sunshine',
 'MAASTRICHT_temp_mean',
 'MAASTRICHT_temp_min',
 'MAASTRICHT_temp_max',
 'MADRID_cloud_cover',
 'MADRID_humidity',
 'MADRID_pressure',
 'MADRID_global_radiation',
 'MADRID_precipitation',
 'MADRID_sunshine',
 'MADRID_temp_mean',
 'MADRID_temp_min',
 'MADRID_temp_max',
 'MUNCHENB_cloud_cover',
 'MUNCHENB_humidity',
 'MUNCHENB_pressure',
 'MUNCHENB_global_radiation',
 'MUNCHENB_precipitation',
 'MUNCHENB_sunshine',
 'MUNCHENB_temp_mean',
 'MUNCHENB_temp_min',
 'MUNCHENB_temp_max',
 'OSLO_cloud_cover',
 'OSLO_humidity',
 'OSLO_pressure',
 'OSLO_global_radiation',
 'OSLO_precipitation',
 'OSLO_sunshine',
 'OSLO_temp_mean',
 'OSLO_temp_min',
 'OSLO_temp_max',
 'SONNBLICK_cloud_cover',
 'SONNBLICK_humidity',
 'SONNBLICK_pressure',
 'SONNBLICK_global_radiation',
 'SONNBLICK_precipitation',
 'SONNBLICK_sunshine',
 'SONNBLICK_temp_mean',
 'SONNBLICK_temp_min',
 'SONNBLICK_temp_max',
 'STOCKHOLM_cloud_cover',
 'STOCKHOLM_humidity',
 'STOCKHOLM_pressure',
 'STOCKHOLM_global_radiation',
 'STOCKHOLM_precipitation',
 'STOCKHOLM_sunshine',
 'STOCKHOLM_temp_mean',
 'STOCKHOLM_temp_min',
 'STOCKHOLM_temp_max',
 'VALENTIA_cloud_cover',
 'VALENTIA_humidity',
 'VALENTIA_pressure',
 'VALENTIA_global_radiation',
 'VALENTIA_precipitation',
 'VALENTIA_sunshine',
 'VALENTIA_temp_mean',
 'VALENTIA_temp_min',
 'VALENTIA_temp_max']
In [186]:
#07.6 variables
variables = [
    'Basel',
    'Belgrade',
    'Budapest',
    'Debilt',
    'Dusseldorf',
    'Heathrow',
    'Kassel',
    'Ljubljana',
    'Maastricht',
    'Madrid',
    'Munchenb',
    'Oslo',
    'Sonnblick',
    'Stockholm',
    'Valentia'
    ]
In [188]:
#07.7 show important variables
important = pd.Series(sumarray, index = variables)
important
Out[188]:
Basel         0.101756
Belgrade      0.055346
Budapest      0.063788
Debilt        0.084828
Dusseldorf    0.102398
Heathrow      0.059113
Kassel        0.029748
Ljubljana     0.089460
Maastricht    0.098919
Madrid        0.068168
Munchenb      0.094360
Oslo          0.056666
Sonnblick     0.034858
Stockholm     0.056693
Valentia      0.003900
dtype: float64
In [190]:
#07.8 show plot
%matplotlib inline

plt.style.use('fivethirtyeight')
# list of x locations for plotting
x_values = list(range(len(important)))
print(variables)

plt.bar(x_values, important, orientation = 'vertical')
plt.xticks(x_values, variables, rotation='vertical')
plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');

['Basel', 'Belgrade', 'Budapest', 'Debilt', 'Dusseldorf', 'Heathrow', 'Kassel', 'Ljubljana', 'Maastricht', 'Madrid', 'Munchenb', 'Oslo', 'Sonnblick', 'Stockholm', 'Valentia']
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In [ ]: